Technology and Problem of Mass Incarceration of Black Americans
Technology and problem of mass incarceration of Black Americans
There is insufficient data on several issues related to mass incarceration of people in American jails. Although the United States has 5% of global population, it contains 25% of people in jails. Yet those being the bars are disproportionately in favor whites, who by far outweigh the Blacks in the country. Besides, there is no single policy in the country, either by the federal or state that guides the issue of incarceration. It has taken a long time before disparities of people in jails and impact of incarceration on their economic wellbeing to be discussed. Data about people that drop out needs to be known for the issue to be addressed adequately. Poor records of people that go to jail, their background and whether these people can contribute to economic development of the country was hardly ever discussed until early 2000s (Pfaff, 2017). There is need to understand these data for appropriate policy to be made.
Poor information systems failed to inform required facts about people that were disappearing from public life (Fleury-Steiner & Longazel, 2013). Incarceration in the US is connected to inequality and poverty that affect lives of especially Black people in the country. Technology contributed to the problem of mass incarceration for failing to point out the glaring inequalities of young people that should have been out in the job market for gainful engagement. They are likely to be involved in crime but even worse, technology applied is based on racial data as opposed to criminal record of a person. CCTV cameras used are also designed to identify Blacks as more likely to be involved in crimes, hence leading to their high possibility of incarceration compared to Whites (Bonds, 2013).
Technology contributed to mass incarceration due to failure to reduce crime rate and poor data collected that tends to be disfavor Blacks. But technology can be used to reduce mass incarceration if it is applied properly. Machine learning software are made in a way that they determine how a criminal should be treated (Leman-Langlois, 2013). Using body-worn camera, computers can identify a person and use their employment and criminal history to determine risk rate and how they can be handled. This would be more appropriate than just reliance on race. Where such data is not used, Blacks arrests are made due to racial prejudice existing in the country.
With the current rate of development of technology, a person deemed to be a certain level of risk can be monitored using several available sensing technology that includes but not limited to GPS, camera, audio, alcohol content, finger print and collection of blood samples for DNA data bank. These can be used instead of prison arrests where the Black race is likely to be more incarcerated. There is need for complete and objective data that provide information about a person on how they behave and whether these behavior predisposes them to future crimes. These include behavior with guns, being public nuisance, usage of drugs among others. These require one to use sensors where they should provide information, which should also be shared to with other law enforcement agencies. However, there is an conflict between what can be collected and level of privacy that one should be entitled to. This could be a form of house arrest where a person’s criminal record has been assessed and is considered dangerous to the public. Such objective data could reduce mass incarceration.
- Bonds, v. E. (2013). Social Problems: A Human Rights Perspective. Allgemeine Nutzungsbedingungen : Routledge.
- Fleury-Steiner, v. B., & Longazel, J. G. (2013). The Pains of Mass Imprisonment. Allgemeine Nutzungsbedingungen : Routledge .
- Leman-Langlois, S. (Ed.). (2013). Technocrime: Technology, Crime and Social Control. New York: Routledge.
- Pfaff, J. (2017). Locked In: The True Causes of Mass Incarceration and How to Achieve Real Reform. New York: Hachette UK. .